Advanced Methods for Steady-State Analysis and Design of Switching Power Converter Systems
Bibliographic record
Abstract
Current development trends in the area of switching power converters such as rising integration, increasing power density, and the growing complexity of power converter systems have indicated a strong need for a new class of simulation tools which would allow for a complex approach to solve the problems of converter analysis and design.Such an approach, besides being a fast and accurate simulation of the switching waveforms of the converter, should also include an overall analysis of converter behavior in response to changes in operating conditions, a search for extreme values of internal variables of the converter circuit, computation of power dissipated by particular devices, calculation of efficiency, ac analysis, sensitivity analysis, and design optimization.The role of determining the steady-state with the resulting algorithms is crucial.Research in the area of numerical analysis of switching power converter circuits has been traditionally focused on the operation of a single power converter.However, growing demand for integrated high-power-density solutions requires this analysis to expand beyond the domain of one converter and capture the interaction of all power converters in the network.In order to better address the needs of development and practical design, this thesis presents alternative simulation techniques which can be used for the analysis of networks consisting of multiple switching converters.The focus of this work is the area of steadystate analysis.Two new methods for the efficient determination of switching power converter steady-state are presented.These new methods simplify the algorithm structure by reducing the number of iteration loops and effectively address the convergence issues Dedication To the living memories of my father, one of the best engineers who I have ever known and whose life has become an example and inspiration for me.I also dedicate this thesis to my mother for her unconditional love, my wife Jana for her kindness, endless patience, and support, and to my children Eva and Tom for being with me even when I was not at home.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".